Worldwide, over a million people die on roads every year. More than 40% of fatal crashes in the U.S. involve alcohol, distraction, drug involvement and/or fatigue. Driver error is believed to be the main reason behind over 90 percent of all crashes.
Over the past years the automobile and technology industries have made significant leaps in bringing computerization into what has for over a century been exclusively a human function: driving. As safety has been a main emphasis in motor vehicles for the past decades, there remains a stubborn and inevitable problem at the heart of the fatalities suffered each year: the driver is human. The automobile, which has followed a path of steady but slow technological evolution for the past 130 years, is on course to change dramatically in the next few years, in ways that could have radical economic, environmental, and social impacts.
New cars increasingly include features such as adaptive cruise control and parking assist systems that allow cars to steer themselves into parking spaces. Companies are attempting to create almost fully autonomous vehicles (AVs) that can navigate highways and urban environments with almost no direct human input.
AVs could enable smarter routing in coordination with intelligent infrastructure, quicker reaction times, and closer spacing between vehicles to counteract increased demand. The first autonomous systems, which are able to control steering, braking, and accelerating, are already starting to appear in cars. Thanks to autonomous driving, the road ahead seems likely to have fewer traffic accidents and less congestion and pollution. Automation could theoretically allow nearly four times as many cars to travel on a given stretch of highway, saving almost three billion gallons of fuel each year in the US alone.
Driverless vehicle technology (or autonomous vehicles—“AV”) promises to reduce crashes, ease congestion, improve fuel economy, reduce parking needs, bring mobility to those unable to drive, and eventually revolutionize travel. Based on current research, annual U.S. economic benefits could be around $25 billion with only 10% market penetration. When including broader benefits and high penetration rates, AVs may save the U.S. economy roughly $430 billion annually. AV operations are inherently different from human-driven vehicles. They may be programmed to not break traffic laws. They do not drink and drive. Their reaction times are quicker and they can be optimized to smooth traffic flows, improve fuel economy, and reduce emissions.
An AV system that relies on optical sensors can be expected to have reliability problems. The signs or markings can be obscured by dirt, ice, or snow and visibility can be impaired by fog, blowing snow, blowing dust, and the like. Furthermore, for night usage, a considerable amount of energy must be expended, either to illuminate the signs or to send out a beam from the sensor.
If autonomous driving is to change transportation, however, it needs to be both widespread and flawless, while also being attractive to the consuming public. Google has developed prototype AV's that have large, unsightly devices on vehicle roofs, featuring huge rotating laser scanners. The driving public desires systems that have style as well as functionality, and thus there is a need for smaller, more limited sensors that can be positioned into the body of a car without compromising weight or styling.
One barrier to large-scale market adoption is the cost of an AV system. Presently, AV technologies include expensive sensors, communication and guidance technology, and software for each automobile. Another problem is that vehicles, at any given time, will vary in the newness of their individual computer systems that regulate the self driving capabilities. For a multitude of vehicles to cooperate on a roadway, there is a need for a coordinated system and method that avoids outdated information.
Another problem presenting designers of AV systems is the threat of a terrorist attack, thus disrupting transportation in particular cities, etc. Centralizing all the sensor elements in a vehicle, rather than having some sensing elements separate from the vehicle, would permit attacks to the vehicle systems to render them unworkable. Conversely, having a more dispersed system where sensors can both be less expensive and that rely on a standard extra-vehicular component (e.g. pavement markings as described herein that work in a system to communicate with vehicle installed systems), one would be better able to avoid the prospect of an attack on particular AV systems. One advantage of certain aspects of the present invention is that disrupting a vehicle's communication or sensors systems would require a more complex and sophisticated attack. Engineering an attack to simultaneously compromise a fleet of vehicles, whether from a point source (for example, compromising all vehicles near an infected AV) or from a system-wide broadcast over infected infrastructure, would pose even greater challenges for a would-be attacker.
Providing AV travel data including routes, destinations, and departure times to centralized and governmentally controlled systems is likely more controversial, particularly if the data is recorded and stored. Without safeguards, this data could be misused by government employees for stalking individuals, or provided to law enforcement agencies for unchecked monitoring and surveillance. Vehicle travel data has wide-ranging commercial applications that may be disconcerting to individuals, like targeted advertising. Decisions to enhance traveler privacy ideally should be balanced against the benefits of shared data. Thus, a system that has at least some elements that are self-contained in the vehicle to frustrate an all inclusive control of such vehicle and/or information being discerned from such vehicle may be desirable.
Some have postulated that there are two basic approaches to autonomous navigation of vehicles on roads: 1) employing a vehicle that navigates like a human, with little pre-existing knowledge of the road features beyond simple maps and general rules of the road; and 2) an approach that relies on extensive prior knowledge of the environment provided by GPS measurements, a map of all stop signs, and pedestrian crossings. Prior inventors have noted the challenging aspects of the first approach due to the extreme variability of real-world environments. Thus, many have pursued systems that involve the second approach, such as the Defense Advanced Research Project Agency (DARPA) Urban Challenge and the Google self-driving car project. While it has been possible to achieve significant reliability using the second approach, such systems require constant updating of detailed prior maps that must be extremely precise and accurate. GPS also does not provide the precision necessary to stay within a lane of traffic; it severely degrades in environments with multipath or shadowing; and the signals can easily be blocked or intentionally disrupted by others desiring to interfere with operation. Inherently, such systems lack the ability to address unexpected changes to the environment where the vehicle is traveling, thus potentially causing significant problems with employment of autonomous vehicles on a wide and large scale. The present invention is directed to the provision of a system that preferably includes both approaches and thus, provides the structured environment and the real time adjustability of a device that can assure the safe transport of people in such self driving vehicles.
Prior art autonomous vehicle systems that sense the local environment and register the sensor measurements to a map of prior observations often require map-matching that depends significantly upon the type of sensor and the locale. Passive visual methods that rely upon digital video cameras perform poorly in outdoor environments due to changes in scene illumination, variations in solar illumination angles, cloudiness, etc. Visual sensing, an approach used by the Google car, require that a sensor transmit light, typically at frequencies that are otherwise relatively dark, and measures the intensity of the return. Algorithms are then used to search for similar intensity patterns in a map of previous measurements to determine the location of the vehicle. But such systems are less than preferred in various adverse weather conditions, such as in snow storms, fog, rain and dust, where important features required to match a scene to prior acquired scenes may be obscured and negatively impact performance. Moreover, other moving vehicles, features that move in wind gusts, etc. present significant challenges, demonstrating that there is still much to be desired and are simply not robust enough to address common real-world conditions. Active sensors employing light detection and ranging (LIDAR) sensors are expensive and due to some precision-engineered electro-optical-mechanical parts, are not believed to be especially practicable solutions.
In certain prior art systems, cameras used for determining a vehicle's position in relation to a lane are limited in robustness and reliability. This can be due to technical limitations of the sensor itself, but also due to external problems, such as poor or absent visible lane markings, caused by road wear, water or snow covering the markings, etc.
Some prior art systems typically utilize at least one of a radar/lidar, DGPS/INS and digital map, or camera/video processing sensor to detect the lane markings (or road edges) that delineate a lane boundary. The detected lane-marking range is typically used to determine the lateral position of the vehicle in the lane (i.e., vehicle in-lane position), and a parameter time-to-lane-crossing is calculated based on the in-lane position and the motion of the vehicle.
There have been attempts to place laser and radar scanners inside front and rear bumpers that sweep the road before and behind for anything within about 200 meters of the car. Some systems employ cameras embedded at the top of the windshield and rear window that track the road markings and detect road signs. Vision scanners near side mirrors are employed to watch the road left and right. Other systems employ ultrasonic sensors above the wheels to monitor the area close to the car and differential Global Positioning System receivers are also used to combine signals from ground-based stations with those from satellites to determine the vehicle's location to within a few centimeters of the closest lane marking. Such existing prototype systems have several computers inside the car's trunk to process data gained from the sensors. Software is employed that may assign a value to each lane of the road based on the car's speed and the behavior of nearby vehicles, in order to decide whether to switch to another lane, to attempt to pass the car ahead, or to get out of the way of a vehicle approaching from behind. Commands are relayed to a separate computer that controls acceleration, braking, and steering. Still other computer systems monitor the behavior of everything involved with autonomous driving for signs of malfunction.
While all such systems are useful in arriving at a commercially viable and cost effective vehicle, much emphasis has been placed on the admittedly important aspects of avoiding collisions with other vehicles and obstacles, spot objects on the road ahead and take control of the brakes to prevent an accident. Such systems may lock onto a vehicle in front and follow it along the road at a safe distance and employ a car's computers to take over not only braking and accelerating, but steering too.
Despite such recent advances and prototypes, the dream of total automation is proving to be surprisingly elusive, largely because the sensors and computers employed are too expensive to be deployed widely. For example, the spinning laser instrument, or LIDAR, seen on the roof of Google's cars, while providing a 3-D image of the surrounding world, accurate down to two centimeters, costs $80,000 and is presently too large for practical use as the consumer will demand more stylish, sleek vehicles.
There is a need for an inertial navigation system that provides precise positioning information by monitoring the vehicle's own movement and combining the resulting data with differential GPS and highly accurate digital maps.
A persistent and as yet largely unaddressed problem relates to poor weather conditions, which can significantly degrade the reliability of sensors. Moreover, it may not always be feasible to rely heavily on a digital map, as so many prototype systems do, as even a very accurate map may be inaccurate and wrong and the work of keeping such maps up to date is a daunting and ongoing task that presents too much liability for wide acceptance of systems so dependent on the same.
While total autonomy of a self driving vehicle may not be imminent—if even desired—there needs to be better systems that are cost effective and that can reduce the number of lives lost, gallons consumed and stress experienced in the evolution of self driving vehicles. As the airline industry appreciates that auto-pilot systems have greatly advanced the safety and reliability of the airline industry, there is still an appreciated aspect of personal human involvement in such vehicles, and hence the need to have experienced and trained airline pilots to work in cooperation with such automated systems. Similarly, in some embodiments, it is envisioned that vehicles may have very useful and beneficial systems that provide various features that can assist in achieving a reduction in the number of lives lost due to the absence of such driving systems.
Another drawback with proposed systems is that they require that essentially all involved vehicles must be provided with transmitter/receivers of similar kinds and types and may rely upon different information systems that are made by different companies. Standardization of such systems may be difficult if not impossible due to the way the technology is developing, the ownership of proprietary rights involved in any given system, etc. For at least some time, the majority of vehicles on the road will simply most probably not be equipped with an active safety system, and thus, even if one vehicle detects a collision risk, others will not. There is thus a need for a system and method that has at least some aspects that are separate and apart from individual vehicles that can be relied upon as a standard by which other competing systems of vehicle makers can rely, to accomplish the general objective of avoiding collisions and saving lives via a cost effective system and method.